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| """Neurosymbolic reasoning implementation.""" | |
| import logging | |
| from typing import Dict, Any, List, Tuple | |
| import json | |
| from .base import ReasoningStrategy | |
| class NeurosymbolicReasoning(ReasoningStrategy): | |
| """Implements neurosymbolic reasoning combining neural and symbolic approaches.""" | |
| async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: | |
| try: | |
| # Neural processing | |
| neural_features = await self._neural_processing(query, context) | |
| # Symbolic reasoning | |
| symbolic_rules = await self._symbolic_reasoning(neural_features, context) | |
| # Integration | |
| integrated = await self._neurosymbolic_integration(neural_features, symbolic_rules, context) | |
| # Final inference | |
| conclusion = await self._final_inference(integrated, context) | |
| return { | |
| "success": True, | |
| "answer": conclusion["answer"], | |
| "neural_features": neural_features, | |
| "symbolic_rules": symbolic_rules, | |
| "integrated_reasoning": integrated, | |
| "confidence": conclusion["confidence"], | |
| "explanation": conclusion["explanation"] | |
| } | |
| except Exception as e: | |
| return {"success": False, "error": str(e)} | |
| async def _neural_processing(self, query: str, context: Dict[str, Any]) -> List[Dict[str, Any]]: | |
| prompt = f""" | |
| Extract neural features from query: | |
| Query: {query} | |
| Context: {json.dumps(context)} | |
| For each feature: | |
| 1. [Type]: Feature type | |
| 2. [Value]: Extracted value | |
| 3. [Confidence]: Extraction confidence | |
| 4. [Relations]: Related concepts | |
| Format as: | |
| [F1] | |
| Type: ... | |
| Value: ... | |
| Confidence: ... | |
| Relations: ... | |
| """ | |
| response = await context["groq_api"].predict(prompt) | |
| return self._parse_features(response["answer"]) | |
| async def _symbolic_reasoning(self, features: List[Dict[str, Any]], context: Dict[str, Any]) -> List[Dict[str, Any]]: | |
| prompt = f""" | |
| Generate symbolic rules from features: | |
| Features: {json.dumps(features)} | |
| Context: {json.dumps(context)} | |
| For each rule: | |
| 1. [Condition]: Rule condition | |
| 2. [Implication]: What it implies | |
| 3. [Certainty]: Rule certainty | |
| 4. [Source]: Derivation source | |
| Format as: | |
| [R1] | |
| Condition: ... | |
| Implication: ... | |
| Certainty: ... | |
| Source: ... | |
| """ | |
| response = await context["groq_api"].predict(prompt) | |
| return self._parse_rules(response["answer"]) | |
| async def _neurosymbolic_integration(self, features: List[Dict[str, Any]], rules: List[Dict[str, Any]], context: Dict[str, Any]) -> List[Dict[str, Any]]: | |
| prompt = f""" | |
| Integrate neural and symbolic components: | |
| Features: {json.dumps(features)} | |
| Rules: {json.dumps(rules)} | |
| Context: {json.dumps(context)} | |
| For each integration: | |
| 1. [Components]: What is being integrated | |
| 2. [Method]: How they are combined | |
| 3. [Result]: Integration outcome | |
| 4. [Confidence]: Integration confidence | |
| Format as: | |
| [I1] | |
| Components: ... | |
| Method: ... | |
| Result: ... | |
| Confidence: ... | |
| """ | |
| response = await context["groq_api"].predict(prompt) | |
| return self._parse_integration(response["answer"]) | |
| async def _final_inference(self, integrated: List[Dict[str, Any]], context: Dict[str, Any]) -> Dict[str, Any]: | |
| prompt = f""" | |
| Draw final conclusions from integrated reasoning: | |
| Integrated: {json.dumps(integrated)} | |
| Context: {json.dumps(context)} | |
| Provide: | |
| 1. Final answer/conclusion | |
| 2. Confidence level (0-1) | |
| 3. Explanation of reasoning | |
| 4. Key factors considered | |
| """ | |
| response = await context["groq_api"].predict(prompt) | |
| return self._parse_conclusion(response["answer"]) | |
| def _parse_features(self, response: str) -> List[Dict[str, Any]]: | |
| """Parse neural features from response.""" | |
| features = [] | |
| current = None | |
| for line in response.split('\n'): | |
| line = line.strip() | |
| if not line: | |
| continue | |
| if line.startswith('[F'): | |
| if current: | |
| features.append(current) | |
| current = { | |
| "type": "", | |
| "value": "", | |
| "confidence": 0.0, | |
| "relations": [] | |
| } | |
| elif current: | |
| if line.startswith('Type:'): | |
| current["type"] = line[5:].strip() | |
| elif line.startswith('Value:'): | |
| current["value"] = line[6:].strip() | |
| elif line.startswith('Confidence:'): | |
| try: | |
| current["confidence"] = float(line[11:].strip()) | |
| except: | |
| pass | |
| elif line.startswith('Relations:'): | |
| current["relations"] = [r.strip() for r in line[10:].split(',')] | |
| if current: | |
| features.append(current) | |
| return features | |
| def _parse_rules(self, response: str) -> List[Dict[str, Any]]: | |
| """Parse symbolic rules from response.""" | |
| rules = [] | |
| current = None | |
| for line in response.split('\n'): | |
| line = line.strip() | |
| if not line: | |
| continue | |
| if line.startswith('[R'): | |
| if current: | |
| rules.append(current) | |
| current = { | |
| "condition": "", | |
| "implication": "", | |
| "certainty": 0.0, | |
| "source": "" | |
| } | |
| elif current: | |
| if line.startswith('Condition:'): | |
| current["condition"] = line[10:].strip() | |
| elif line.startswith('Implication:'): | |
| current["implication"] = line[12:].strip() | |
| elif line.startswith('Certainty:'): | |
| try: | |
| current["certainty"] = float(line[10:].strip()) | |
| except: | |
| pass | |
| elif line.startswith('Source:'): | |
| current["source"] = line[7:].strip() | |
| if current: | |
| rules.append(current) | |
| return rules | |
| def _parse_integration(self, response: str) -> List[Dict[str, Any]]: | |
| """Parse integration results from response.""" | |
| integrations = [] | |
| current = None | |
| for line in response.split('\n'): | |
| line = line.strip() | |
| if not line: | |
| continue | |
| if line.startswith('[I'): | |
| if current: | |
| integrations.append(current) | |
| current = { | |
| "components": "", | |
| "method": "", | |
| "result": "", | |
| "confidence": 0.0 | |
| } | |
| elif current: | |
| if line.startswith('Components:'): | |
| current["components"] = line[11:].strip() | |
| elif line.startswith('Method:'): | |
| current["method"] = line[7:].strip() | |
| elif line.startswith('Result:'): | |
| current["result"] = line[7:].strip() | |
| elif line.startswith('Confidence:'): | |
| try: | |
| current["confidence"] = float(line[11:].strip()) | |
| except: | |
| pass | |
| if current: | |
| integrations.append(current) | |
| return integrations | |
| def _parse_conclusion(self, response: str) -> Dict[str, Any]: | |
| """Parse final conclusion from response.""" | |
| conclusion = { | |
| "answer": "", | |
| "confidence": 0.0, | |
| "explanation": "", | |
| "factors": [] | |
| } | |
| mode = None | |
| for line in response.split('\n'): | |
| line = line.strip() | |
| if not line: | |
| continue | |
| if line.startswith('Answer:'): | |
| conclusion["answer"] = line[7:].strip() | |
| elif line.startswith('Confidence:'): | |
| try: | |
| conclusion["confidence"] = float(line[11:].strip()) | |
| except: | |
| conclusion["confidence"] = 0.5 | |
| elif line.startswith('Explanation:'): | |
| conclusion["explanation"] = line[12:].strip() | |
| elif line.startswith('Factors:'): | |
| mode = "factors" | |
| elif mode == "factors" and line.startswith('- '): | |
| conclusion["factors"].append(line[2:].strip()) | |
| return conclusion | |